Fast, accurate, and objective detection of disease imaging biomarkers is crucial for the study and diagnosis of ophthalmic diseases. In recent years, much work has been focused on the automatic segmentation of retinal and corneal layer structures seen on spectral domain optical coherence tomography (SDOCT) images. OCT systems have been equipped with segmentation software, which have been mainly targeted to measure the nerve fiber layer and the total retinal thicknesses.
Aside from layered structures, there is a need for automatically segmenting closed-contour features in ophthalmic images and other types of images. Examples of closed-contour anatomical and pathological structures include such as cysts seen on SDOCT images of diabetic or pediatric retina with edema, and retinal pigment epithelium (RPE) cells seen on confocal microscopy fluorescence images of flat-mounted mouse retina. Previous techniques, including active contours, have been demonstrated to segment closed-contour features; however, such techniques have limited accuracy and cannot be optimally extended to linear features, such as retinal layers.
In view of the foregoing, there is a need for improved techniques and systems for segmenting and identifying closed-contour features in images.